no code implementations • 26 Jan 2024 • Andrey Styskin, Fedor Romanenko, Fedor Vorobyev, Pavel Serdyukov
In this paper, we propose a web search retrieval approach which automatically detects recency sensitive queries and increases the freshness of the ordinary document ranking by a degree proportional to the probability of the need in recent content.
1 code implementation • NeurIPS 2019 • Dmitrii Emelianenko, Elena Voita, Pavel Serdyukov
The dominant approach to sequence generation is to produce a sequence in some predefined order, e. g. left to right.
no code implementations • 20 Jun 2019 • Valentina Fedorova, Gleb Gusev, Pavel Serdyukov
We study the problem of aggregation noisy labels.
no code implementations • ACL 2018 • Elena Voita, Pavel Serdyukov, Rico Sennrich, Ivan Titov
Standard machine translation systems process sentences in isolation and hence ignore extra-sentential information, even though extended context can both prevent mistakes in ambiguous cases and improve translation coherence.
1 code implementation • ICML 2018 • Boris Sharchilev, Yury Ustinovsky, Pavel Serdyukov, Maarten de Rijke
We address the problem of finding influential training samples for a particular case of tree ensemble-based models, e. g., Random Forest (RF) or Gradient Boosted Decision Trees (GBDT).
1 code implementation • ACL 2017 • Alexander Fonarev, Oleksii Hrinchuk, Gleb Gusev, Pavel Serdyukov, Ivan Oseledets
Skip-Gram Negative Sampling (SGNS) word embedding model, well known by its implementation in "word2vec" software, is usually optimized by stochastic gradient descent.
no code implementations • 13 Dec 2016 • Alexey Drutsa, Andrey Shutovich, Philipp Pushnyakov, Evgeniy Krokhalyov, Gleb Gusev, Pavel Serdyukov
We develop a novel approach to build intent-aware user behavior models, which overcome these limitations and convert to quality metrics that better correlate with standard online metrics of user satisfaction.
1 code implementation • NeurIPS 2016 • Alexander Shishkin, Anastasia Bezzubtseva, Alexey Drutsa, Ilia Shishkov, Ekaterina Gladkikh, Gleb Gusev, Pavel Serdyukov
This study introduces a novel feature selection approach CMICOT, which is a further evolution of filter methods with sequential forward selection (SFS) whose scoring functions are based on conditional mutual information (MI).
no code implementations • 28 Nov 2016 • Alexey Drutsa, Gleb Gusev, Pavel Serdyukov
We investigate video popularity prediction based on features from three primary sources available for a typical operating company: first, the content hosting provider may deliver its data via its API, second, the operating company makes use of its own search and browsing logs, third, the company crawls information about embeds of a video and links to a video page from publicly available resources on the Web.
no code implementations • 16 Oct 2016 • Alexander Fonarev, Alexander Mikhalev, Pavel Serdyukov, Gleb Gusev, Ivan Oseledets
Cold start problem in Collaborative Filtering can be solved by asking new users to rate a small seed set of representative items or by asking representative users to rate a new item.